VARIABILITY AND PREDICTABILITY OF SEASONAL RAINFALL OVER CENTRAL OROMIA, ETHIOPIA

Abstract

With its diverse climates and ecosystems, Central Oromia encompasses the Peak Mountains, plateaus, highlands, plains, lakes, rivers and the Great African Rift Valley. The study area had been affected by seasonal rainfall variability that lead to shorter rainy period, excess and deficit rainfall, and weather disasters resulting to drastically threatened agricultural, water resources and socio-economic activities during anomalous years. This study focused on characterizing rainy season, evaluating variability, and examining predictability of the rainy season. The study important for generating scientific findings, which is essential for effective water resource management, agricultural planning, and disaster preparedness. Forty years of stations’ data from 1981–2020, gridded rainfall, and global data were utilized. Statistical analysis to characterize and identify rainfall variability, Correlation and composite analysis to test predictability, multiple linear regression, and linear discriminant analysis techniques to develop seasonal forecasting model applied. The forecasting model skill assessed using relative operating characteristics and a ranked probabilistic skill score. The study area is classified into four homogeneous rainfall regimes, namely Regime I, Regime II, Rime III, and Regime IV, based on the rainfall annual cycle and inter-annual variability. Seasonal rainfall characteristics show early onset over west parts and late onset over Great Rift Valley, and the reverse is true for cessation. The length of the growing period is longer over west parts but very shorter over low lands. The greater variability noted and a strong irregular rainfall distribution over RII. The Atlantic, Nino 3.4, and West Indian Ocean had a significance influence on Regimes I and IV; however, the West Indian and Atlantic had on Regimes II and III during main rainy season. For second rainy season Regimes I and II by Nino-3.4, and West Indian Ocean, whereas Regimes III and IV by the Atlantic, Nino-3.4, and the West Indian Ocean. Models show different forecasting skill for each regime. The study suggests that in order to understand the dynamics of climate variability, future research should concentrate on the elements causing unseasonal rainfall events during the dry season. In order to close the gap in rainfall characteristics, current variability, and notable predictability at the local level, this finding should prompted to the central Oromia.